{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GeForce GT 750M (CNMeM is enabled with initial size: 50.0% of memory, cuDNN 5005)\n"
     ]
    }
   ],
   "source": [
    "import theano\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import MDBN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding a layer with 799 input and 40 outputs\n",
      "Adding a layer with 16046 input and 400 outputs\n",
      "Adding a layer with 400 input and 40 outputs\n",
      "Adding a layer with 15418 input and 400 outputs\n",
      "Adding a layer with 400 input and 40 outputs\n",
      "Adding a layer with 120 input and 24 outputs\n",
      "Adding a layer with 24 input and 3 outputs\n"
     ]
    }
   ],
   "source": [
    "reload(MDBN)\n",
    "rna_DBN, ge_DBN, me_DBN, top_DBN =MDBN.load_network('Exp_2016-11-10_1536_run_0.npz','../MDBN_run/Run_2016-11-10_1536')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datafiles = MDBN.prepare_TCGA_datafiles('../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "RNA_output, _ = MDBN.output_DBN(rna_DBN, datafiles['mRNA'], datadir='../data')\n",
    "GE_output, _ = MDBN.output_DBN(ge_DBN, datafiles['GE'], datadir='../data')\n",
    "ME_output, _ = MDBN.output_DBN(me_DBN, datafiles['ME'], datadir='../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 119.5, 384.5, -0.5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8ZuJWvZ6zlPWcFURLq/fiiy9u0q9+9aubtGwKVhvaZhqRBuVSE87PQus8naxn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1374944d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joint_layer = np.concatenate([RNA_output, GE_output, ME_output],axis=1)\n",
    "plt.imshow(joint_layer, cmap='gray',interpolation='none')\n",
    "plt.axis('tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x123b9d4d0>,\n",
       "  <matplotlib.axis.XTick at 0x123c24910>,\n",
       "  <matplotlib.axis.XTick at 0x123cc7510>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N+XvcFK1cqrRMDiLl8kWwGv4eV6GY77KDtJlZjTknPSCP9MrlEV41lk656n3ZVhaUk97J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\n0mZmNeYOVj7VAAAAC0lEQVQgbWZWY/8fWmxzPAC2Su0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123875bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_output = top_DBN.get_output(theano.shared(joint_layer,borrow=True))\n",
    "plt.imshow((top_output>0.8)*np.ones_like(top_output)-(top_output<0.2)*np.ones_like(top_output),interpolation='none',extent=[0,3,385,0])\n",
    "plt.colorbar()\n",
    "plt.axis('tight')\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x123eab110>,\n",
       "  <matplotlib.axis.XTick at 0x123cc7b90>,\n",
       "  <matplotlib.axis.XTick at 0x123f5b8d0>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mks4BFpBNoDwzIpYVDcjK44pwPYb28wI+1euJunuRbstPAU9JemfRnRbptvyC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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123aa86d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(top_output, interpolation='none',extent=[0,3,385,0])\n",
    "plt.axis('tight')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 244.,    1.,    1.,    0.,    0.,    0.,    0.,    0.,    0.,  139.]),\n",
       "  array([ 147.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    1.,  237.]),\n",
       "  array([ 219.,   14.,    0.,    7.,    3.,    7.,   12.,    6.,    4.,  113.])],\n",
       " array([  6.62467414e-10,   1.00000001e-01,   2.00000001e-01,\n",
       "          3.00000000e-01,   4.00000000e-01,   5.00000000e-01,\n",
       "          6.00000000e-01,   7.00000000e-01,   8.00000000e-01,\n",
       "          9.00000000e-01,   1.00000000e+00]),\n",
       " <a list of 3 Lists of Patches objects>)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R5EudrHgyyfsGUGbfJbklyViSJ6bpc/y5WVV9Wxh/Qfk+cA4wBDwGXHhMn6uAL3bW3ww8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\nH/gVpdlZ4j9SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12cc73f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 244.,    1.,    1.,    0.,    0.,    0.,    0.,    0.,    0.,  139.]),\n",
       " array([  4.25631397e-09,   9.99999323e-02,   1.99999860e-01,\n",
       "          2.99999788e-01,   3.99999716e-01,   4.99999645e-01,\n",
       "          5.99999573e-01,   6.99999501e-01,   7.99999429e-01,\n",
       "          8.99999357e-01,   9.99999285e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O1c2JnuHXAW7ASvLBxafrmqr61yS/k+R7wP+xeCnjsDPIXAB/DrweuLo7s91TVWtnN+rJ\nGHAuXvKSqQ9ySgb8GvlukjuAh4GfA9dU1bYZDnsiBvx7sQW4LslDLMbwz6rqx7Mb9WQkuRGYA45P\n8jiLn046ihG76Y1XktQI/xeHktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9Jjfh/qM6c\na3yUr9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123da5d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "code = (top_output[:,0:3] > 0.5) * np.ones_like(top_output[:,0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import find_unique_classes\n",
    "U = find_unique_classes(code)\n",
    "cl = U[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  4.,  1.,  1.,  1.,  4.,  4.,  1.,  2.,  4.,  1.,  1.,  3.,\n",
       "        1.,  1.,  1.,  4.,  1.,  3.,  1.,  2.,  1.,  1.,  4.,  1.,  4.,\n",
       "        2.,  1.,  1.,  1.,  0.,  2.,  4.,  1.,  2.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  4.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  2.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  4.,  1.,  1.,  2.,  1.,  4.,  4.,  2.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  4.,  4.,  3.,\n",
       "        1.,  1.,  1.,  1.,  2.,  1.,  4.,  1.,  4.,  1.,  4.,  1.,  4.,\n",
       "        1.,  1.,  4.,  4.,  0.,  4.,  4.,  1.,  1.,  1.,  1.,  3.,  3.,\n",
       "        1.,  4.,  1.,  1.,  1.,  1.,  4.,  1.,  1.,  1.,  4.,  4.,  3.,\n",
       "        1.,  2.,  1.,  1.,  1.,  3.,  4.,  1.,  4.,  1.,  1.,  3.,  4.,\n",
       "        4.,  1.,  1.,  1.,  0.,  1.,  4.,  1.,  4.,  3.,  1.,  1.,  2.,\n",
       "        4.,  4.,  1.,  3.,  1.,  4.,  1.,  1.,  1.,  4.,  1.,  4.,  1.,\n",
       "        2.,  1.,  1.,  4.,  1.,  4.,  1.,  1.,  4.,  2.,  3.,  4.,  1.,\n",
       "        4.,  1.,  1.,  4.,  1.,  4.,  4.,  0.,  1.,  1.,  1.,  1.,  4.,\n",
       "        1.,  1.,  3.,  1.,  1.,  4.,  1.,  1.,  1.,  3.,  1.,  2.,  3.,\n",
       "        1.,  4.,  1.,  4.,  2.,  1.,  4.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,  3.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,  1.,  4.,  2.,  4.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  1.,  4.,  1.,  4.,  1.,\n",
       "        1.,  4.,  1.,  1.,  1.,  1.,  1.,  3.,  4.,  1.,  4.,  1.,  2.,\n",
       "        1.,  4.,  2.,  1.,  4.,  1.,  3.,  1.,  1.,  1.,  2.,  1.,  4.,\n",
       "        1.,  1.,  1.,  1.,  4.,  3.,  1.,  0.,  1.,  3.,  1.,  1.,  1.,\n",
       "        1.,  1.,  4.,  0.,  1.,  1.,  2.,  4.,  1.,  1.,  1.,  4.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  2.,  1.,  1.,  1.,  3.,  1.,  1.,  3.,\n",
       "        1.,  4.,  1.,  3.,  1.,  3.,  2.,  1.,  1.,  1.,  1.,  2.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,  1.,  1.,  4.,\n",
       "        4.,  1.,  1.,  2.,  1.,  1.,  4.,  1.,  1.,  1.,  1.,  3.,  3.,\n",
       "        4.,  4.,  4.,  4.,  4.,  3.,  4.,  3.,  4.,  4.,  4.,  4.,  3.,\n",
       "        4.,  0.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,\n",
       "        4.,  4.,  4.,  4.,  4.,  3.,  4.,  4.])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x12cb1f4d0>,\n",
       "  <matplotlib.axis.XTick at 0x12abe9fd0>,\n",
       "  <matplotlib.axis.XTick at 0x1327c2a10>,\n",
       "  <matplotlib.axis.XTick at 0x1327e9210>,\n",
       "  <matplotlib.axis.XTick at 0x1327e9950>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dkhrx/6YmtTJsNfSOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1237d0950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cl,bins=5)\n",
    "plt.xticks((0.4,1.25,2.0,2.8,3.6),('0','1','2','3','4'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check Survival curves for the different classes\n",
    "==============================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "id=[]\n",
    "with open('../data/'+datafiles['mRNA']) as f:\n",
    "    my_csv = csv.reader(f,delimiter='\\t')\n",
    "    id = my_csv.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stat={}\n",
    "with open('../data/TCGA_Data/data_bcr_clinical_data_patient.csv') as f:\n",
    "    reader = csv.reader(f, delimiter='\\t')\n",
    "    for row in reader:\n",
    "        patient_id=row[1]\n",
    "        stat[patient_id]=(row[15],row[16],row[17])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following case IDs were  not found in clinical data\n",
      "TCGA-24-0981\n",
      "No data for TCGA-04-1519\n",
      "No data for TCGA-04-1360\n",
      "TCGA-01-0639\n",
      "TCGA-01-0642\n",
      "No data for TCGA-04-1357\n",
      "TCGA-01-0628\n",
      "TCGA-04-1331\n",
      "TCGA-01-0636\n",
      "TCGA-01-0633\n",
      "TCGA-01-0637\n",
      "TCGA-01-0631\n",
      "TCGA-01-0630\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "time_list = []\n",
    "event_list = []\n",
    "group_list = []\n",
    "print('The following case IDs were  not found in clinical data')\n",
    "for index, key in enumerate(id[1:]):\n",
    "    m = re.match('TCGA-\\d+-\\d+', key)\n",
    "    patient_id = m.group(0)\n",
    "    if patient_id in stat:\n",
    "        patient_stat = stat[patient_id]\n",
    "        add_group = True\n",
    "        try:\n",
    "            time_list.append(float(patient_stat[2]))\n",
    "            event_list.append(1)\n",
    "        except ValueError:\n",
    "            try:\n",
    "                time_list.append(float(patient_stat[1]))\n",
    "                event_list.append(0)\n",
    "            except ValueError:\n",
    "                print('No data for %s' % patient_id)\n",
    "                add_group = False\n",
    "        if add_group:\n",
    "            group_list.append(cl[index])\n",
    "    else:\n",
    "        print(patient_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12ad2aa90>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xo76+3tK1YacYiogbwA7owGYzgI8A3GuM2Rbk+v8E8Lox5o8BXuMUwwzQ3Ky5\n8upqa9dHus2br+5uTefceKM+LyyMrnwukZ3FNMXQGNMDYCGA5QC2AqgzxmwTkQdF5O8DvSWm1pLj\nFRfr4KfVRTmxLAZyu3Xu+vLlwOuvc5cgyjxc7ENxZwzwxz8CnZ1ASYmuuAzHW/XQV6RTEI8d08+u\nqgKuvjqyNhPZGasYUtIdOaJpjoYGa9u8BRLNKs+zZ/WHwZQp/c+73cCECdamQhLZTaggzl0NKSEq\nKjRgdnVpj7ygIPJ7LFkS+fL6vDzg9Glg69b+58+c0d8KKiu1qBZRuuD/zpQwBQU6e+XIEQ2i0Ygm\nX+6dR+575OYCK1cCra3RtYPIrphOoYQ6dw748EPg0CENrnl5kd8j1uJZXs3Nmp/P8vz+OXgwcPnl\nsd2TKBmYE6eU++AD3e5twoTo3u878BlNzRVAywOcP9//8bXXAkOGRNcmomRhECdbePll7QnHOo87\nlnnlXr29wOHDmre/+24W0SJ74/ZsZAslJcDRo7rC0nt0dKSmLS6XTkXs7AQ2b05NG4jigT1xSpr2\ndp0C6HX4sNY9KS/v21PTCm9qJdq0iq9Tp/Q3gylT9IdMSUls9yNKBKZTyJZOndLBxo8/1l5xpOIx\n4NndrTNWzp4Fpk/Xg8huGMTJtrq7gZdeimyrN1/xmrly8qROg8zJ0Tz5tddqrXIiO2BOnGzL5QLy\n86PPjce67ZtXaWnfpsydnZq75zZw5AQM4pRSLpeu7rTDDvZutx4FBcAnnwDvvZfqFhGFx2X3lHLZ\n2UBbm/6Znx/5+3337ozHYGdpqd6nsRF4553+r+XlAXPm9C0YIko15sQp5c6dA9av1zKy2dl95/Pz\nrVVA9BWvHDmgg53+vyG0tQG33x5dLRiiaHFgk2zv1CktXOXV1qbpjKFDI5t+CMRnMVAwe/boTkbc\nkIqSiUGcHOfMGd2rc8MG7Y0XFFivPhiP2uTBdHZqZcYvfKHvnMuldVi46pMShUGcHMkYHWBsbNS0\nRixT/uKVZunu1qqMvj9QenuBO++MrrgXkRUM4uRon32mlRD9N2SOlH8PPV6988ZG4Etfin7zC6Jw\nuCkEOdollwC7dvXtat/bCwwfHvl9/AN2pBtOBNPVBTQ1MYhTarAnTo5w7pyWjz13Dli6FBg1KvZ7\nxivFcvw4UFSkeXFfublak4W5cooV0ymUNnp6gLo6nX4oEvvS+HgE8p4eXbbv7+xZ4J57OKecYscg\nTmnDGGB9FEUgAAAPMklEQVT7dt3QYds2DeK5ubHdM1FTEhsbdXs630HQrCztncdaU50yC3PilDZE\ngMmT9XF7O7B7NzBmTGwpC++Kz3gNdHoNGqRVGn11dOgALYM4xQt74uRof/qTpi2iKWXrL551yoNp\nadGB2nBBXEQrOxYVJaYd5CxMp1DaOn4ceOON+Ax0eiVyxaf/xhjBnDql0xZHj05MO8hZmE6htFVU\npDlnb2DMztZKhLFIVHoFsL57UG+vpl6OHAn8uts9cDYMZSb2xMnRenuBDz7Quivd3Rr4hg2Lz70T\nuXw/nPZ2XeIfjAhwxx3MrWcKplMoIzQ1AfX1OoMl2p2CwklkqiUSLS3A/PkDg7gIpzSmIwZxygje\nFMTSpQNnq7hcwJAhsada4lnqNhaNjYGDdW4ucOut/Uv6kvMxiFPGMEZ75D09/c83NOjc8vJy69UQ\ng7FLbzyQlhZNs8Q6d57shQOblDG8U/P8nT4N7NypKytjXeXpu5OQ93mqe+Zevb26CCrQbxwTJzK4\npyMGccoIkyfrwOeWLVp/BdDnI0dGfq9EFdKKh6Ii3bjCX2en/nBjEE8/DOKUMaZM0WXwgFYefP11\n3dE+1ql6xcWaK7dDbzzY9MXu7uS2g5KHu91TxnC5dOOGvDztsc6Zo8Hcfx/NSC1ZMnAqot309AAr\nVgwsA0DOx544ZSQRYNw43Zy5qSm6tIov/zy595wdeueA1l9vadEfWpReGMQpo82apVMSW1q0hz5o\nUHT3CRSs7ZQr9+rs1FIFgM4xZ20W57M0xVBEagH8BJp+ec4Y8yO/178G4FHP01MA/sEYsyXAfTjF\nkGyltxc4elRnrXz0UV9+PC8v9s0cUrniM5CTJ/vKE/T06Lz5efNS0xaKTExTDEXEBeBpADcAaAKw\nXkT+bIzZ7nPZXgDXGmNOegL+rwHMib3pRInlcum2avn5mnLwLhg6dy76XrmX3XrnpaV6AH07JZHz\nWUmnzAawyxizHwBEpA7AbQAuBHFjzFqf69cCSNCiZ6LEKCkBrr9eHzc06FTEs2fjV4fFK1DuPNh1\nie6xnzmjgTzWVayUWlaC+AgAB32eN0IDezDfAPBmLI0iSqWJE7U++TvvDCwb63LFVnTKamBOdI89\nOxtoa9MceXFxYj+LEiuuA5siMhfA3wL4YrBrFi1adOFxTU0Nampq4tkEopgVFGh6ZcSIgUH8yBGg\noiLx1QPD9dhj7am7XEBhYfTvp8Sqr69HfX29pWvDDmyKyBwAi4wxtZ7njwEwAQY3pwF4FUCtMSbA\nmjEObJLz/fnPOk1vyJDUtiMe9VuamnSaZU6OrmjNz49P2yj+Qg1sWlnssx7ABBGpFpEcAF8F0K8P\nICKjoQH8/mABnCgdXHyxtZ15nMC7B+jWrVpbhpwpbDrFGNMjIgsBLEffFMNtIvKgvmx+BeB/ACgH\n8IyICIAuY0yovDmRI40cCXz8sc4r9+ru1hx6MgcIg6VbIkmzFBTo4a0lQ87EUrREETBGBwR9l+q/\n954umrHDLjvRpFmam3UWTk6O1peJdWolxR9L0RLFicjAUra5uboKMt7TEaMRTQ998GCdG3/iBFBd\nzSDuNAziRDG67DLg/fcHLp4RiX0DikgFC9ShZrrk5OjBvLgzMYgTxcg7JfHEif7nT53Snm2yA3m0\njAF27AD27Ut1S4Jzu4EZM7QsAinmxIkS5JVXdCWo/4BnKlZIWtkbtKvL/jNv2tuBW26JfXcmp2FO\nnCgFhgzpqxjo1dmpOehkz8lesiT8KtDsbPtvsMyUz0AM4kQJEmgx8qpVGsgper29Ov6QijEHO2IQ\nJ0qivDydY25lJ6Dc3PimDey8wbNVxgDLl+vjrCzgS1/ibBrmxImSqLvb2n6X+/YBmzZpmdxEicfS\n/VRqaQFuvDH1JRCSgTlxIpvIytLDynWdnf1XhvoyRuelx7pxBTkfgziRDQ0fDtx0U/DX163THr3d\nByIp8RjEiWzIW9ckGJdLt1vzH9hzuTIrR9zZqd9DsmVl2aeUL4M4kQNdconOmfa3Zw9w/rw96rgk\nWlYWsGZNaj67oKBv7n2qMYgTOdBFFwU+39Rk/R7e2SpOnKUCpG5As7cXOHYsNZ8dCIM4URrJydEA\n459m6e4Gysv7LzLyBu5Ubt5MsWMQJ0oj112ny+f9bdyoJXQDrRR1eo880zGIE6WRvLzAxaGysrQ3\nbszAaYnskTsbgzhRBigt1c0fWluBoUNT3RrnM8Y+1R4ZxIkywJQpuoz/44+DXxNsQwk7sUPKx+XS\n32bWrk1tO7wYxIkySEdH3+OcnP61WVIdHK2wyw8ZO/02wyBOlCEqK3XgE9D8eENDattD8cEgTpQh\niov1AIAzZxjE0wWr8RJlIO888nPnUtsOih2DOFEGys3VHYb8N3cm52E6hShDuVw65TAnRwO6Eyoi\n+s6gscNMFTtgECfKUNOmAePH63znkyd1Lrnd+QZtu8xUSTWmU4gy1NChwJgxWpHv/Hnd6b63N9Wt\nokgxiBNluPJyTU2cPQscP57q1lCkGMSJMtyECUBtLTBpki4nJ2dhTpyIAABut1ZADLavpx10d+sg\nbKBqjJmKQZyIAGiPvLo61a0Ibe3avu3YUlnrxU4zYxjEiQiAFnWy+7ZuvmV0UxlE7TQzhjlxIiIH\nYxAnInIwplOIyDGysoD2dqCzU0sGlJVxkFNMEucUiYhJ5ucRUXo5e1YPANiyRee1p2Kl6YIFwKlT\nyfxEgTFGAr1iqScuIrUAfgJNvzxnjPlRgGt+BuBmAKcB/I0xhoUuiSiufPcQTWWtl2QPqoYaSA2b\nExcRF4CnAdwEYAqAe0Vkkt81NwMYb4y5CMCDAH4RQ3ttob6+PtVNsITtjD+ntJXt1MVJvb3xOz75\npN7SdXZKKFgZ2JwNYJcxZr8xpgtAHYDb/K65DcCLAGCMWQegVEQq49rSJOM/kPhySjsB57Q109tZ\nWKiLk44di9/x4Yf1Ya9pbQX270/IXykqVtIpIwAc9HneCA3soa455Dl3OKbWEREFMXWqHvG0dStw\n992hr+ntBf7wh/h+biw4O4WIKEI5OfYpTxB2doqIzAGwyBhT63n+GADjO7gpIr8AsMoY85Ln+XYA\n1xljDvvdy0aZJCIi54hldsp6ABNEpBpAM4CvArjX75olAB4C8JIn6Lf5B/BQjSAiouiEDeLGmB4R\nWQhgOfqmGG4TkQf1ZfMrY8wyEblFRHZDpxj+bWKbTUREQJIX+xARUXwlrXaKiNSKyHYR2Skijybr\nc0O0Z5+IbBKRjSLykedcmYgsF5EdIvK2iJT6XP+4iOwSkW0iMi+B7XpORA6LyGafcxG3S0Rmishm\nz/f9kyS29Yci0igin3iO2lS3VURGisi7IrJVRLaIyHc85231vQZo57c95231nYpIrois8/zb2Soi\n/8dz3lbfZ5i22uo7jYkxJuEH9IfFbgDVALIBNACYlIzPDtGmvQDK/M79CMA/eR4/CuDfPY8vAbAR\nmn4a4/m7SILa9UUA0wFsjqVdANYBmOV5vAzATUlq6w8B/GOAayenqq0AhgGY7nlcBGAHgEl2+15D\ntNOO32mB5083gLUArrbb9xmmrbb7TqM9ktUTt7JgKNkEA38TuQ3AC57HLwC43fN4AYA6Y0y3MWYf\ngF0YOFc+LowxfwFwIpZ2icgwAMXGmPWe6170eU+i2wrod+vvtlS11RjTYjxlIIwxHQC2ARgJm32v\nQdo5wvOy3b7TTs/DXOi/oxOw2fcZpq2Azb7TaCUriAdaMDQiyLXJYgCsEJH1IvINz7lK45lVY4xp\nAVDhOR9sMVOyVETYrhHQ79gr2d/3QhFpEJHf+PxKbYu2isgY6G8PaxH5f++ktdWnnes8p2z1nYqI\nS0Q2AmgBUG+M+Qw2/T6DtBWw2XcarUyuJ361MWYmgFsAPCQi10ADuy+7jvratV0A8AyAccaY6dB/\nNP+R4vZcICJFAF4B8LCnp2vL/94B2mm779QY02uMmQH9jeYaEamBTb9Pv7ZeKyLXwYbfabSSFcQP\nARjt83yk51zKGGOaPX8eBfAaND1yWDw1Xzy/Ph3xXH4IwCiftye7/ZG2K2XtNcYcNZ6kIYBfoy/t\nlNK2ikgWNDD+zhjzZ89p232vgdpp1+/U07Z2aH74Ctjw+wzQ1jcAXGHn7zRSyQriFxYMiUgOdMFQ\nynbIE5ECT28HIlIIYB6ALZ42/Y3nsr8G4P3HvgTAV0UkR0TGApgA4KNENhH983URtcvzq+xJEZkt\nIgLgAZ/3JLStnn+8XncC+NQmbX0ewGfGmJ/6nLPj9zqgnXb7TkVkiDf9ICL5AL4EHQy03fcZpK0N\ndvtOY5KsEVQAtdDR9l0AHkvm6G2AtoyFzpDZCA3ej3nOlwN4x9PO5QAG+bzncehI9TYA8xLYtsUA\nmgCcA3AAunCqLNJ2Abjc83fbBeCnSWzriwA2e77f16B50pS2FTobocfnv/knnv8fI/7vnci2hmin\nrb5TAFM9bdsIYBOA70f77ycJ/+2DtdVW32ksBxf7EBE5WCYPbBIROR6DOBGRgzGIExE5GIM4EZGD\nMYgTETkYgzgRkYMxiJMjiUipiPyD53GViMRl61pPidJ/9Dz+nyJyfTzuS5QonCdOjuQpEPW6MSau\n+52LyA8BnDLG/N943pcoUdgTJ6d6EsA4T0H/P4jIFgAQkb8WkT+Jbk6wV0QWisj3PNetEZFBnuvG\nicibniqW74nIRP8PEJH/FJE7PY8/F5FFIvKx6GYiEz3nC0Q3x1jreW1+Er8DIgZxcqzHAOwxWony\nB+hfMW8KtNbzbAD/BqDdc91aaM0LAPgVgIXGmFme9z9r4TOPGGMuB/ALAN/3nHsCwEpjzBwA1wP4\nsadGB1FSWNntnshpVhndCKBTRE4AWOo5vwXAVE/Rs6sAvOwpZgTojlPh/Mnz58cA7vA8ngdgvoj8\nwPM8B1qxc0eMfwciSxjEKR2d83lsfJ73Qv+fdwE44emdR3PfHvT92xEAXzbG7IqyrUQxYTqFnOoU\ngGLP40DbbAVljDkF4HMRuct7TkSmRdmOtwF8x+c+06O8D1FUGMTJkYwxxwF8ICKbATyF4LvIBDt/\nH4Cve7bn+hS6D2So9wa7z/8GkC26C/oWAP8rfOuJ4odTDImIHIw9cSIiB2MQJyJyMAZxIiIHYxAn\nInIwBnEiIgdjECcicjAGcSIiB2MQJyJysP8PRceYIjJZV5UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ad21550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(time_list,event_observed=event_list)\n",
    "kmf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ny9lKdVIQl8h1dxfWG5/RdDDjZKAoq1dEqpWCuFTcT/9pWsYp/FGvuZK+l2ec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\nkv6GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bc68910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T=np.array(time_list)\n",
    "E=np.array(event_list)\n",
    "ix = (np.array(group_list) == 1)\n",
    "kmf.fit(T[ix], E[ix], label='group 1')\n",
    "ax=kmf.plot()\n",
    "for i in [4]:\n",
    "    ix=(np.array(group_list)==i)\n",
    "    kmf.fit(T[ix], E[ix], label='group %d' % i)\n",
    "    kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9VtjPDIc9/p8DRznnqon+ov0kx/H0mJmVAA8D/9v7Bhvqn7EE/QntMXLOHXDO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\n/wWkRfSqgVtQiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bc4a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T=np.array(time_list)\n",
    "E=np.array(event_list)\n",
    "ix = (np.array(group_list) == 0)\n",
    "kmf.fit(T[ix], E[ix], label='group 0')\n",
    "ax=kmf.plot()\n",
    "for i in range(2,4):\n",
    "    ix=(np.array(group_list)==i)\n",
    "    kmf.fit(T[ix], E[ix], label='group %d' % i)\n",
    "    kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison with MATLAB results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('/Users/gluca/SoftwareProjects/Thesis/MDBN/MATLAB_classes.txt') as f:\n",
    "    matlab_classes = f.readlines()\n",
    "ML = []\n",
    "for s in matlab_classes:\n",
    "    ML.append(float(s))\n",
    "ML = np.asarray(ML)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class ids obtained from MATLAB and from Theano are not the same, the formula below happens to convert them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_cl = np.mod(ML+3,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  4.,  1.,  1.,  1.,  4.,  4.,  1.,  1.,  4.,  1.,  1.,  4.,\n",
       "        1.,  1.,  0.,  4.,  1.,  4.,  1.,  1.,  1.,  1.,  4.,  1.,  4.,\n",
       "        4.,  1.,  1.,  1.,  4.,  4.,  4.,  1.,  1.,  1.,  1.,  4.,  1.,\n",
       "        1.,  1.,  4.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  1.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  4.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,\n",
       "        1.,  1.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,  4.,  4.,  4.,  4.,\n",
       "        1.,  1.,  1.,  1.,  4.,  1.,  4.,  1.,  4.,  1.,  4.,  1.,  4.,\n",
       "        1.,  1.,  4.,  4.,  4.,  4.,  4.,  1.,  1.,  1.,  1.,  4.,  4.,\n",
       "        1.,  4.,  1.,  1.,  1.,  1.,  4.,  1.,  1.,  1.,  4.,  4.,  4.,\n",
       "        1.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,  4.,  1.,  1.,  4.,  4.,\n",
       "        4.,  1.,  1.,  1.,  4.,  1.,  4.,  1.,  4.,  4.,  1.,  1.,  1.,\n",
       "        4.,  4.,  1.,  4.,  1.,  4.,  1.,  1.,  1.,  4.,  1.,  4.,  1.,\n",
       "        1.,  1.,  1.,  4.,  1.,  4.,  1.,  1.,  4.,  1.,  4.,  4.,  1.,\n",
       "        4.,  1.,  1.,  4.,  1.,  4.,  4.,  4.,  1.,  1.,  1.,  1.,  4.,\n",
       "        1.,  1.,  4.,  1.,  1.,  4.,  1.,  1.,  1.,  4.,  1.,  0.,  4.,\n",
       "        1.,  4.,  1.,  4.,  4.,  1.,  4.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,  4.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,  1.,  4.,  1.,  4.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  1.,  4.,  1.,  4.,  1.,\n",
       "        1.,  4.,  1.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,  4.,  1.,  1.,\n",
       "        1.,  4.,  1.,  1.,  4.,  1.,  4.,  1.,  1.,  1.,  4.,  1.,  4.,\n",
       "        1.,  1.,  1.,  1.,  4.,  4.,  1.,  4.,  1.,  4.,  1.,  1.,  1.,\n",
       "        1.,  1.,  4.,  4.,  1.,  1.,  4.,  4.,  1.,  1.,  1.,  4.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  1.,  4.,  4.,\n",
       "        1.,  4.,  1.,  4.,  1.,  4.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        4.,  1.,  1.,  1.,  1.,  1.,  1.,  4.,  4.,  1.,  1.,  1.,  4.,\n",
       "        4.,  1.,  4.,  1.,  1.,  1.,  4.,  1.,  1.,  1.,  1.,  4.,  4.,\n",
       "        4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,\n",
       "        4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,\n",
       "        4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   3.,  226.,    0.,    0.,  156.]),\n",
       " array([ 0. ,  0.8,  1.6,  2.4,  3.2,  4. ]),\n",
       " <a list of 5 Patch objects>)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GqHPu45nkd4GV7t1dmNI7jlkH/jHgxQPXd3Rtw30uHtFn1kbWWVUnTr0VrKovAs9J8sIz\nV+JYFmEsR1qUsUyyjX6Ifq6qbluly0KM56g6F2U8B+r5H/p/L+TXhm5aiPE8Za06F2Q8rwLekOQ/\ngb8DXpvks0N9Njyesw78bwAvS3JJknOAtwDDZ5tvB94Op7+h+3hVrcy4rmEj6xxcG0uym/5HWn94\nZsvsH561X+0XYSxPWbPOBRrLzwCHquoTa9y+KOO5bp2LMJ5JfinJed32LwC/Tf/DD4PmPp7j1LkI\n41lV11XVi6vqpfTz6O6qevtQtw2P50z/lk6t8QWsJO/p31x/U1X/kuT1Sb4NPAG8a5Y1bbZO4A+S\n/AlwEvg/4M1nus4kN9P/c34vSvIdYB9wDgs0luPUyWKM5VXA24CD3XpuAdfR/6TWwoznOHWyAOMJ\n/AqwP0noP4c+V1X/umjP9XHqZDHGc1WTjqdfvJKkRnjSVpIaYeBLUiMMfElqhIEvSY0w8CWpEQa+\nJDXCwJekRhj4ktSI/wf6LekrErwyWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bbaea10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(new_cl,bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of samples with the same class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "319"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(new_cl == cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8285714285714286"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "319./385."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
